2. 运输和导航：医院校园面积大，志愿者会驾驶高尔夫球车，接送患者、访客和员工，帮老人减少步行负担，避免迷路——这对行动不便的老年患者来说，格外实用。
The simplest approach is to check every single point. Compute the distance from the user's location to every restaurant in the database, keep the ones that are close enough, and throw away the rest.
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There’s a secondary pro and con to this pipeline: since the code is compiled, it avoids having to specify as many dependencies in Python itself; in this package’s case, Pillow for image manipulation in Python is optional and the Python package won’t break if Pillow changes its API. The con is that compiling the Rust code into Python wheels is difficult to automate especially for multiple OS targets: fortunately, GitHub provides runner VMs for this pipeline and a little bit of back-and-forth with Opus 4.5 created a GitHub Workflow which runs the build for all target OSes on publish, so there’s no extra effort needed on my end.
人 民 网 版 权 所 有 ，未 经 书 面 授 权 禁 止 使 用。51吃瓜对此有专业解读
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I then added a few more personal preferences and suggested tools from my previous failures working with agents in Python: use uv and .venv instead of the base Python installation, use polars instead of pandas for data manipulation, only store secrets/API keys/passwords in .env while ensuring .env is in .gitignore, etc. Most of these constraints don’t tell the agent what to do, but how to do it. In general, adding a rule to my AGENTS.md whenever I encounter a fundamental behavior I don’t like has been very effective. For example, agents love using unnecessary emoji which I hate, so I added a rule: